> monitor-experiment
Monitor running experiments, check progress, collect results. Use when user says "check results", "is it done", "monitor", or wants experiment output.
curl "https://skillshub.wtf/wanshuiyin/Auto-claude-code-research-in-sleep/monitor-experiment?format=md"Monitor Experiment Results
Monitor: $ARGUMENTS
Workflow
Step 1: Check What's Running
ssh <server> "screen -ls"
Step 2: Collect Output from Each Screen
For each screen session, capture the last N lines:
ssh <server> "screen -S <name> -X hardcopy /tmp/screen_<name>.txt && tail -50 /tmp/screen_<name>.txt"
If hardcopy fails, check for log files or tee output.
Step 3: Check for JSON Result Files
ssh <server> "ls -lt <results_dir>/*.json 2>/dev/null | head -20"
If JSON results exist, fetch and parse them:
ssh <server> "cat <results_dir>/<latest>.json"
Step 4: Summarize Results
Present results in a comparison table:
| Experiment | Metric | Delta vs Baseline | Status |
|-----------|--------|-------------------|--------|
| Baseline | X.XX | — | done |
| Method A | X.XX | +Y.Y | done |
Step 5: Interpret
- Compare against known baselines
- Flag unexpected results (negative delta, NaN, divergence)
- Suggest next steps based on findings
Step 6: Feishu Notification (if configured)
After results are collected, check ~/.claude/feishu.json:
- Send
experiment_donenotification: results summary table, delta vs baseline - If config absent or mode
"off": skip entirely (no-op)
Key Rules
- Always show raw numbers before interpretation
- Compare against the correct baseline (same config)
- Note if experiments are still running (check progress bars, iteration counts)
- If results look wrong, check training logs for errors before concluding
> related_skills --same-repo
> run-experiment
Deploy and run ML experiments on local or remote GPU servers. Use when user says "run experiment", "deploy to server", "跑实验", or needs to launch training jobs.
> research-review
Get a deep critical review of research from GPT via Codex MCP. Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental results.
> research-refine
Turn a vague research direction into a problem-anchored, elegant, frontier-aware, implementation-oriented method plan via iterative GPT-5.4 review. Use when the user says "refine my approach", "帮我细化方案", "decompose this problem", "打磨idea", "refine research plan", "细化研究方案", or wants a concrete research method that stays simple, focused, and top-venue ready instead of a vague or overbuilt idea.
> research-refine-pipeline
Run an end-to-end workflow that chains `research-refine` and `experiment-plan`. Use when the user wants a one-shot pipeline from vague research direction to focused final proposal plus detailed experiment roadmap, or asks to "串起来", build a pipeline, do it end-to-end, or generate both the method and experiment plan together.